# Statistical Physics

Statistical physics is a field of physics that studies the behaviors of large collections of interacting objects. Traditionally, these objects have been atoms, molecules, magnetic spins, or volumes of fluid, but in recent decades, statistical physicists have been studying many other types of interacting groups of “objects”, such as species in ecosystems, traders in financial markets, chemicals in cardiac and neural tissue, electrical activity within neural circuits, and grains in sand piles. Using various mathematical techniques, statistical physicists attempt to describe, understand, and predict the macroscopic behaviors of the collections (such as the phase of matter, transport or structural properties, spatial or temporal patterns, etc.) without a detailed knowledge of the behavior of each individual object. For collections in equilibrium or near equilibrium, much of the behavior is well-understood; however, for collections of particles far from equilibrium (for example, when input and output of energy is not balanced at every moment of time), the underlying rules governing the behaviors are still being discovered.

At Georgetown, we are studying a variety of classical and quantum systems using experiments, computation, and theory. Most of our work involves trying to understand situations that are either far from equilibrium or subjected to imposed disorder. The objects of our studies are wide ranging and include atoms, magnetic spins, biopolymers, biological cells, granular material, colloids, fluids, and electrical activity within neural circuits.

Emanuela Del Gado — statistical mechanics and computational physics; structure, cooperative dynamics and nonlinear mechanics of amorphous solids, gels and glasses; nanoscale structure and mechanics of cement gels; self-assembly of nanoparticles and fibrils at liquid interfaces; biomimetic coatings and mechanics of tissues.

Rhonda Dzakpasu — spatio-temporal pattern formation in in vitro neural systems, extracellular multi-electrode array recordings, computational modeling of coherent activity in neural networks, development of non-linear methods of data analysis

David Egolf — statistical physics of nonequilibrium dynamical systems, including fluids, granular media, cardiac and neural tissue, and biopolymer networks; effective theories of QCD.

Jim Freericks — strongly correlated electrons (charge and thermal transport and nonequilibrium effects), transport in multilayered nanostructures, resonant inelastic X-ray scattering, ultracold atoms in optical lattices (especially mixtures, dipolar molecules, and the Hubbard model), undergraduate understanding of quantum mechanics, student satisfaction with the major.

Peter Olmsted – soft matter physics and biophysics: non-equilibrium physics, phase transitions, and (nano)mechanics; theory and computer simulations; shear banding and microfluidics, polymers, elastomers, surfactants, colloids, liquid crystals; protein, motor, and lipid bilayer membrane dynamics and mechanics.